TY - GEN
T1 - Feature selection and combination for stress identification using correlation and diversity
AU - Deng, Yong
AU - Hsu, D. Frank
AU - Wu, Zhonghai
AU - Chu, Chao Hsien
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Using multiple physiological sensors to detect different stress level has become an important and popular task in improving human health and well-being. In the process, the selection of a smaller set of independent features is a necessary, yet challenging, step for feature combination, situation analysis and decision making. In this paper, we investigate feature selection methods using both concepts of correlation and diversity. Six feature combination methods (C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, K Nearest Neighbors and Combinatorial Fusion) are applied to the selected features in the detection of the stress levels. Our results demonstrated that (a) diversity based feature selection is as good as correlation based selection across all six combination methods, and (b) combinatorial fusion method performs better than five other combination methods across all features selected by using both correlation and diversity.
AB - Using multiple physiological sensors to detect different stress level has become an important and popular task in improving human health and well-being. In the process, the selection of a smaller set of independent features is a necessary, yet challenging, step for feature combination, situation analysis and decision making. In this paper, we investigate feature selection methods using both concepts of correlation and diversity. Six feature combination methods (C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, K Nearest Neighbors and Combinatorial Fusion) are applied to the selected features in the detection of the stress levels. Our results demonstrated that (a) diversity based feature selection is as good as correlation based selection across all six combination methods, and (b) combinatorial fusion method performs better than five other combination methods across all features selected by using both correlation and diversity.
UR - http://www.scopus.com/inward/record.url?scp=84874589587&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874589587&partnerID=8YFLogxK
U2 - 10.1109/I-SPAN.2012.12
DO - 10.1109/I-SPAN.2012.12
M3 - Conference contribution
AN - SCOPUS:84874589587
SN - 9780769549309
T3 - Proceedings of the 2012 International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012
SP - 37
EP - 43
BT - Proceedings of the 2012 International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012
T2 - 12th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2012
Y2 - 13 December 2012 through 15 December 2012
ER -